{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pima Indians Diabetes Data Set数据探索\n",
    "\n",
    "数据说明：\n",
    "Pima Indians Diabetes Data Set（皮马印第安人糖尿病数据集） 根据现有的医疗信息预测5年内皮马印第安人糖尿病发作的概率。   \n",
    "\n",
    "数据集共9个字段: \n",
    "0列为pregnants(怀孕次数)；\n",
    "1列为Plasma_glucose_concentration(口服葡萄糖耐量试验中2小时后的血浆葡萄糖浓度)；\n",
    "2列为blood_pressure(舒张压,单位:mm Hg）\n",
    "3列为Triceps_skin_fold_thickness(三头肌皮褶厚度,单位：mm）\n",
    "4列为serum_insulin(餐后血清胰岛素,单位:mm）\n",
    "5列为BMI,体重指数（体重（公斤）/ 身高（米）^2）\n",
    "6列为Diabetes_pedigree_function(糖尿病家系作用)\n",
    "7列为Age(年龄)\n",
    "8列为Target(分类变量,0或1）\n",
    " \n",
    "数据链接：https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes\n",
    "\n",
    "p.s.: Kaggle也有一个Practice Fusion Diabetes Classification任务，可以试试:)\n",
    "https://www.kaggle.com/c/pf2012-diabetes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. import 工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#首先 import 必要的模块\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"pima-indians-diabetes.csv\")\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检查数据规模\n",
    "读取测试数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Train :', (768, 9))\n"
     ]
    }
   ],
   "source": [
    "print(\"Train :\", train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据基本信息\n",
    "#total values in each column, null/not null, datatype, memory occupied etc\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "该数据集已知存在缺失值，某些列中存在的缺失值被标记为0。通过这些列中指标的定义和相应领域的常识可以证实上述观点，譬如体重指数和血压两列中的0作为指标数值来说是无意义的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看数值型特征的基本统计量\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从结果中我们可以看到很多列的最小值为0。而在一些特定列代表的变量中，0值并没有意义，这就表名该值无效或为缺失值。\n",
    "\n",
    "具体来说，下列变量的最小值为0时数据无意义：\n",
    "1、血浆葡萄糖浓度\n",
    "2、舒张压\n",
    "3、肱三头肌皮褶厚度\n",
    "4、餐后血清胰岛素\n",
    "5、体重指数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第1、2、5列中0值较少；相比较而言，第3、4列中的0值多出数倍，接近总量的一半。\n",
    "为了确保有足够的数据量来训练模型，针对不同的列需要有不同的缺失值判断策略。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 查看每个变量的分布 及其与标签之间的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Number of occurrences')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FwEqePOUkSZqj+lzFNPxciJ3APcCqkVQjSZoYfcYgfC6EJM1D0z1y9J3T7FdVdcEI6pEk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e7a6c90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#该问题为分类问题，类别型特征直方图可用countplot\n",
    "sns.countplot(train['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 怀孕次数pregnants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Number of occurrences')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9m5kNbmV6Mf0KOJ909/TS5oZjZmbtokyCeDMiftj0SMzMrK2USRD/Kulk4Fbgre7CiJjd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\nQZiZWSEnCDMzK+QEYWZmhZwgzMys0P8HPt1D7EV57wEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e37f050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "### Number of occurrences\n",
    "sns.countplot(train['pregnants'])\n",
    "plt.xlabel('Number of pregnants')\n",
    "plt.ylabel('Number of occurrences')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#怀孕次数有超过17的？\n",
    "#但在疾病判断案例中，异常值可能就意味着得病，不能删除\n",
    "ulimit = 10\n",
    "\n",
    "#删除怀孕次数大于10的样本\n",
    "train = train[train['pregnants'] < ulimit]\n",
    "print (train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c419910>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e315d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"pregnants\", hue=\"Target\",data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "怀孕次数和是否得病好像还真有关系！！！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plasma_glucose_concentration\n",
    "血浆葡萄糖浓度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Number of occurrences')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c57a150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Plasma_glucose_concentration, kde = False)\n",
    "plt.xlabel('Plasma_glucose_concentration')\n",
    "plt.ylabel('Number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TqvqWqj4BnA5cFZvQksvUqVNZvmwZVT1PSKohsk4F8jpT2204M2bMiPsSx8ZE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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e69a790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Plasma_glucose_concentration', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Plasma_glucose_concentration', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### blood_pressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'frequency')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c7c7450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.blood_pressure, kde = False)\n",
    "plt.xlabel('blood_pressure')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "血压为0？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看blood_pressure与标签之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JwIkE1oSqA8I2KzwWtGvXDoC4uiqcWGBZ3Z69OtfUHZktJJsjri6QLLKyWs7S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\n0/27AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c7c7e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='blood_pressure', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('blood_pressure', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Triceps_skin_fold_thickness\n",
    "三头肌皮褶厚度（单位：mm）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'frequency')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0ZuCWgfnTgL/tt/sHwAljqWp0/h64pKr+PfCbdNu+oPd1kiXAScDyqno23YUtx7Iw9/VH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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c929990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Triceps_skin_fold_thickness, kde = False)\n",
    "plt.xlabel('Triceps_skin_fold_thickness')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like there are some outliers in this feature. So let us remove them and then plot again.\n",
    "但在疾病判断案例中，异常值可能就意味着得病，不能删除\n",
    "\n",
    "ulimit = 80\n",
    "train = train[train['Triceps_skin_fold_thickness'] < ulimit]\n",
    "\n",
    "plt.scatter(range(train.shape[0]), train[\"Triceps_skin_fold_thickness\"].values,color='purple')\n",
    "plt.title(\"Distribution of Triceps_skin_fold_thickness\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1wRtHVV7XRmVc3qmAhYsWM3Xq1JCUWZ6/I7jPr2B/TdssaqWioiJGjhzFjh27\nKOh4ckjGUlSXs/nhONO7MHXqVKZNmxbucIwJCJfLxcPjHiI11s11hxaE9OrvHwXRFLsdFLsdrMqJ\n4Y+CwE3jU5VBh5RydLNS3nzjDdauXRuycsH/3lBPicgp5XeIyEPAhYEPKbI5nU7uHz2a1WtWU9h+\nEJ7kpuEOqXIilLYZQFnD1jw/fjxz5swJd0TG1NiHH37I5swtXNU5j+QIHpkdaCJweedCkmKUF8Y/\nH9IZG/xNFkOA1/Y2eovIE3gH0p0QrMAiUVlZGePGPcyihQspbnMM7kYBuwoXXL4utZ7kZjz88CP8\n+OOP4Y7ImGorLi7m32+/Rc/GTnpGwOyxoZYUo1zQroBlvy5n/vz5ISvX366zK4DzgXdEZApwPHCC\nqkbW5CVB5PF4ePzxx5k791vvWIr0gEyLFTqOaAo7nkxZQiPuv380ixYtqvo1xkSgb775hsKiYoa0\nCV07RaQZ2KKUBnHw8cehW2K5sq6z+3eRTcA7JcdJwDigZ027ztYWe7vnffHFF5S27IOr+WHhDql6\nor0z4LpiU7j77nv49ddfwx2RMQftq6++onmi0jm1rOqD66hoBxzTtJh5834M2Qy1ldUsDtRV9lqg\nDHiJ4HadjSgTJ07k448/prR5D5wteoY7nJqJjqew8yk4o+IZMXJkyBvJjKmpdWtX0ym1NBKGNIVV\n54YuPB5PyOaOqqzrbEXdZYPSdTZSvfvuu97R2U274sw4IiIG3dWUxiRS0OlUit0O7rxreETNbGlM\nZYqKisjOyaNFoq3dsvczyMzMDEl51RpFJiIDRWRAoIOJNN999x0vv/IKrkZtKW09IKiJIu6Pn4gq\n2k1U0W4SVs0k7o+fglYWgMYlU9jpFPKLihk+YgSFhYVBLc+YQIiO9nZT9Wjt/9FWU3s/g9jY0Cz3\n4+84i29EZKBv+y7gI+AjERkZzODCKTMzk0ceeRRPYmNK2h8X9BqFo2gP4nYhbhfR+dtxFAV/TidP\nQkMK259IZmYmjz/+hC2cZCJebGws8XFx5JRGzmwJ4ZLj9H4GKSkpISnP30/8cLwr5QHcAAwC+gP/\nCkJMYefxeHjs8ccpcbkp6nBiQNfOjjTuBi0obdmHuXO/5auvvgp3OMZUqUfPHizPibwZE0Jt+Z4Y\noqOiOPTQQ0NSnr/JwgF4RKQ9EK2qK1T1DyB0q/uE0Ndff83yX3+lOONINC453OEEnbP54XiS03nh\nxRdt0kET8Y46agDbC4UNeVHhDiVsyjywYFc8PXv2JDExNNON+Jss5uGdOvwJYBqAL3HUuXEWqsrb\n//43mpiGq0mncIcTGuKgpGUPg2QTAAAfoElEQVRfcrKz+eyzz8IdjTGVOuWUU0hJTmLaxqRwhxI2\n322LY1excOFFF4WsTH+TxVVACbAaGO3b1w14IQgxhdW6dev4fdMmStO71omeT/5yN2iBJqbx+ezZ\n4Q7FmEolJydzyaWXsXR3DL9kxYQ7nJDLcwofbUqmW7dD6d+/f8jK9XcE9y5VHaGq96pqgW/fJ6r6\n9N5jRCRi15w4GEuWLAGgrGGd7xX8F87UVqxauZKSktq7HKWpHy644AI6dmjPa6sakFNaf37UqcJr\nq5Ip8kRz113DkRD+oA1kl4I6MU/UH3/8gcQmoLGhuQ4YSTyJaagqmzdvDncoxlQqNjaW+0ePwUkM\n45en4qwnwy7+uymBJVmx3HTTv2jfvn1Iy7b+Z/vJz89HI3BtilDY+74LCgrCHIkxVWvTpg333Hsf\n6/OieHlFCp4Q9fwuLhPi4+O54IILiI+Pp7gsNL/uv90ax7SNiZx22mmcd955ISmzPEsW+4mOjka0\nnq4q5xtnERVVf3uZmNrl+OOPZ9iwm1mUFcuklckhSRhFZcKQIUMYNmwYZ555JkUhSBbztscyeXUy\n/Y48krvuuiukl5/2qrsDCKqpQYMG4Kqf1+zF5Z3FMzU1NcyRGOO/888/n8LCQiZPnoxb4YZDC4gK\n4s/gxGjlk08+QVX59NNPaRYd3Az1/bZYJq1KoWePnjzw4IP7RrGHWiBLrROtTM2bN0fLnIirBI2J\nD3c4IeUozUdEaNasWbhDMeagXHHFFURFRTFp0iRK3cLQ7vnEBqmCnBCtlBSU8OGHH3ofNwxespi9\nOZ6pa5Po06cPDz/yCPHx4ftOCmT+fTyA5wqbNm3aAOAozg5zJKHnKM4mvWmzsP5BGlNdl112Gbfe\neitLdsfy+NJUCly19/erKry/PoEpa5M45phjeOTRR8P+/9LfuaFuFZFevu1+IrJBRNaIyL5Ovqo6\nLlhBhlKnTt6BeI6iOjfesEoxJXvo0rmeDEQ0ddJ5553HmDFj2VQQx7jFjdhVXPuaZcs8MHFlMh//\nnsiQIUN44MEHiYsLf6cbfz/JO4FNvu3HgAnA08DzQYgprNLS0miSnk5Uwa5whxJaZSVQnBeyeWaM\nCZZBgwbx5FNPk08yDyxuxPq82tM0W+gSnlyayg/b47jmmmu48847I6bDib/JoqGq5ohIMtALeE5V\nXwW6Bi+08Dn8sMOILdq5r3dQfRBVsBOA7t27hzkSY2quV69evPjSSyQ1bMqjv6SyaFfkj/TeVezg\nocWNWJcfx7333ssVV1wRll5PFfE3WWT6LjldBHynqm4RSQFqNBRGRFqJyNcislJEVojIrb79aSLy\nhYis9d03qkk5B6tXr15oaSFSmh/KYsMqOm8b0dExdO1aJ/O/qYfatGnDS6+8SodOXRj/awNm/REf\nsb//1udG88DiRuSTzJNPPc3gwYPDHdJf+JssRgAfAw/iXX8bYAiwoIbllwF3quqhwFHAUBHpBowC\n5qhqJ2CO73HIHHHEEQBE54ZmBSoA3M4/DfTB7Qxd2UBs/lZ69OgREddGjQmURo0a8exzzzPwuIH8\n37okpq5NDNngPX8t2hXDo0saktSoGRNefplevXqFO6QD8nduqE9UtamqZqjq3gQxDTi3JoWr6jZV\nXezbzgdWAi2Bc4C3fIe9VdNyDlZGRgYZrVoRk/17yMqUMuefBvpIWeiShaM4B4qyOfbYY0JWpjGh\nEh8fz9ixD3DhhRcyOzOBF5enRMz0IF9mxjF+eQPad+zESy+/QuvWkTsnnd8tP74pyS8EDgG2Ah+o\n6vpABSIibYHewM9AM1XdBt6EIiJNA1WOv04ZPJjJkycjJXlofIOgl6fRsX8a6KPRoZubKiZrLQ6H\ng+OPPz5kZRoTSg6Hg6FDh9K0aVNeemkCTy5N5Y4eeSQEeUBdRVRh2sYE/rspkaMHDOD+0aNJSEgI\nSyz+8rfr7N+BX4F+eNspjgSW+vbXmK/h/EPgNlXNO4jXXS8iC0Vk4a5dge29dPrppxMVFUXsjhUB\nPW+FomIpKfEO9CkpKYGo0Kyri9tJXNYajj12II0bNw5NmcaEyYUXXsh9993PuvxYHl3SkDxn6BuQ\nPQpT1yby303eeZ4efOihiE8U4H+bxSPAEFU9X1XvUNUL8LZZPFbTAEQkBm+imKqqH/l27xCRFr7n\nWwA7D/RaVZ2oqn1VtW96enpNQ/mT9PR0TjvtNGKzViMlfuevWid22zK0rJTLLrs03KEYExInnXQS\n48Y9zNbiOB5f0pD8ECYMVXh7TRKzMxO48MILGTFiRNim7zhY/iaLVOD7/fb9ANRopXDx9gt7HVip\nqs+Ue2oGcKVv+0ogLGtlXHPNNcTFxpLw+7w62Y3WUbSH+B0rGDx4MF26dAl3OMaEzIABA3j0scfY\nURrH40sbhmS0typMWZvIV1viueSSS/jXv/6Fw1F7Bg36G+nzwEMiEg8gInF4e0Y9V8PyjwEuB04U\nkSW+2xl4ayyDRWQtMJgA1GCqo3Hjxtw8bBhReVuJ3bYsHCEEj9tJ4oZvaNAghaFDh4Y7GmNC7ogj\njmDcww+zrTiGZ5cFf02M6ZsS+CIzgYsuuojrr78+osZQ+MPfZHEN3lHcOSKyBcgF7gKu8U39sUFE\nNhxs4ar6vaqKqvZQ1V6+20xV3a2qJ6lqJ9/9noM9d6CceeaZnHDCicRtWUT0nk3hCiOwPB4S1n9D\nVGkeY0aPpmHDhuGOyJiw6NevH/fedz9rc6N4LYhTnM/bHstHGxMZPHgwN910U61LFOB/b6hrgxpF\nBBMRRo0aybbt21i9+lsKo6Jxp2aEO6zqUw/xG78lOjeT2+64gz59+oQ7ImPCatCgQVx33XVMmjSJ\n9qllnNYqsEsUbCmM4vXVKfTocTjDh4d2KdRA8itZqOqcYAcSyeLi4njyiSe49bbb2LDuS4rbDaIs\nrW24wzp4HjcJG74mOvsPbrjhBs4+++xwR2RMRLj00ktZ/uuvvD//Jw5r5CIjOTDXpMo88MpvDUhK\nSmHs2AeIjQ1RL8cg8LfrbKyIPOCbaXaPb99gEbkpuOFFjpSUFJ5/7jm6dT2UhA1fExOqLrWBUlZC\n0prPic7+g5tvvplLLrkk3BEZEzFEhOEjRpCUnMLba5IDdt4vMuP5Pd/BXSNGkpaWFrDzhoO/bRbP\nAEcA/yz3mpVAvWoZTUlJ4ZlnnuaYo48m/o+fids0DzwRMhS0Eo7ibFJWfUps8W5Gjx7N+eefH+6Q\njIk4aWlpXH7lVazKiWZVds27szrdMHNzEn369ObYY48NQITh5W+yOB+4WFW/AzwAqpoJ1OKL99UT\nHx/Pgw8+yMUXX0zsrlUkrfl833KkkSg6+3eSV35Capzw3HPPcuKJJ4Y7JGMi1pAhQ2jYIIUvt9R8\noaGFu2LJLYXLL78iAJGFn7/JwrX/sSLSBAhbL6VwioqK4sYbb+S+++4jrnQPyStn4Cg44LjB8FEl\nNnMRCevm0LFDOyZNnMhhhx0W7qiMiWhxcXEc2f8oVuXG1Xho1crsGJKTEunRo0dgggszf5PFB8Ab\nItIKQETSgfHAu8EKrDY4+eSTeWnCBJo2TCFp9WdE71oT7pC8ypwkrP2SuG1LOf3003nxhRdo2jTk\n02sZUyt17dqVvFLIreHI7s2FMXTu0jViFi+qKX+Txd3ANmAN0BD4A9gNjA1OWLVHp06dmDTxVXr3\n7EnCpu+J27wgrKO9pTSf5FWfEJu/ldtuu40RI0bYtOPGHASXywVAXA2/42MdHsp856oL/J2ivFRV\nhwGJeKcQT1LVm1W1NKjR1RKpqak8+eQTnHvuucRu/5X4Dd+AekIeh6Mom5RVn5DkcPH0009x7rnn\n1to+3caEy+bNm4mNgviomv3oS431sCVzM2VlZQGKLLz87Tp7mYj0UK9tquoRkR4iYrPP+URHR3Pr\nrbdy/fXXE7NnI/Hrvw1pDcNRnEPymlk0TE7g5Zdeonfv3iEr25i6Ij8/ny+//IL+TUuo6e+so5qV\nsjs7h3nz5gUmuDA7mFln9182botvv/ERES699FJuuOEGYrI3ErttaWgKdjtJWj+HBklxvDB+PG3a\ntAlNucbUMRMnTqS01MngjJqP4u7V2EV6gjLx1VcoKCgIQHThdTCzzubsty8HCOna2LXFxRdfzEkn\nnUTc1l9Cso537LZfoSSPBx94gIyMeteb2ZiAmDFjBh9//DFD2hTTNqXm46eiHHBd1zy2bd3KuHEP\n4XZH/pisyvibLFYC5+2372xgVWDDqRtEhBtuuAGHOIgJdg8pVeKzVjPw2GPp2bNncMsypg5SVT78\n8EOee/ZZejR2cUH7ooCdu2ujMi7rVMBPP/3MmNGjKS6O3DFZVfF3mOJI4BMRuQhYD3QETgXOClZg\ntV3Tpk1Ja5zGNmeQq5+eMtRVQteuXYNbjjF1UFlZGS+88ALTp0+nTxMnN3bLxxHgPiEntSzFrcL/\nzfuBm28exiOPPForu7L72xtqLtAT79KqjYFlQE/ffnMA27ZtIysrC098anALckRDXBJLl9ax9TaM\nCbL169dz0403Mn36dM5oXcwth+cTH4RF60Tg1FYl3H54Hpmb1nPN1Vcxe/ZstJYtqOb3Mk2qulFV\nx6nqDb77TUGMq1YrKiri/vtHI45oXI07BrcwEUqbdGH+/J95//33g1uWMXWAy+Xi7bff5obrr2fH\n5nXcfFg+F3csCniNYn+9mrh4oG82LaLzeeSRR7j3nnvIysoKbqEBVGEeFZGXVPVfvu03gAOmQVW9\nJkix1Urr1q1j9JgxbN26laKOJ6NxgZvBsiLOFj2IKspiwoQJbN68maFDh9pAPGMOYP78+bww/nk2\nZ27hqKalXN65kJTY0P3Cb5Ho4d4+OXy+OZ4P5v/I5f+4jCuvuprzzz+fmJiYkMVRHZVVuraW296/\n26zZT15eHv/+97/56KOP8ETFUdT5NNwNWoSmcHFQ3P5E4rYsZMaMGcxfsICbbryR4447zgblGQNs\n2bKFCRMmMG/ePJolKnf0yKdXk/CMrnYInN66hD5NnExdl8Qrr7zCJx/P4OZbbqV///5hickfFSYL\nVR0HICJRwFrgPVUN7BJSdUBOTg7Tpk3j/Q8+oKiwEGeTzjgzjkBjEkIbiMNBaat+lDVoybbM+YwZ\nM4auXbvyj3/8g6OPPrpWLQxvTKAUFhYyZcoUPnj/PRy4uahDIae2KiEmAv47NEv0cEePfJZmxTB1\nnTJy5Ej69+/H0KHDaN26dbjD+4sqm3NU1S0iL6jq26EIqLbYvHkz06ZN45NPPsXpLKWsYStKuw/G\nkxjeBU7cqS0paHAOMVlrWbVpGffddx+tWrXm73+/iJNPPpn4+JpPvWxMpPN4PMyaNYtJE18lOyeX\nY5qXcGGHYtLiQj8NT1V6NnHRLW0PX2bG89/F87n66qs477y/ceWVV5KSkhLu8Pbxt+3/UxE5Q1Vn\nBjWaCOfxeJg/fz4ffvgRCxbMB4cDV6P2OFscjichgsYnigNXehdcTToRvWcjf+xYzlNPPcXLL7/C\nWWcN4ZxzzqFFixBdIjMmxDZt2sRTTz7J8hUr6Jjq5pa+BXRoENnzM8U4vJemjmleyocbEvnwgw/4\nas6X3HLrbRx//PERcTnZ32ThAD4Ske+BzZRr7K4PDdz5+fnMmjWLDz/6iO3btiGxiZQe0htX0y5o\nTGK4w6uYOChr3IGCtPZEFezAteM33nn3Xd59910GDBjA3/72N4444oiI+EM0pqZcLhdTpkxh6pQp\nxEe5ubZrAQNblNZ4jqdQahCrXN21kBNaljB5tYexY8cy4KijuP2OO8I+NsPfZLEWeDKYgUSirKws\n3nvvPabPmEFpSQmelKaUth9EWaM24KhFc9SL4E5pjjulOaXOQmJ2ruTHBYuZN28ebdu24x//uIxB\ngwYRHR2ETubGhEB2djaj77+PX5ev4OhmpVzaqZAGIezlFGhtU9yM6ZPN7Mx4PlrwE9dd+08eGvdw\nWBdSksoGhojIJar6nxDGU219+/bVhQsXBuRc+fn5vPnmm/x3+nTcbjeuRu1wNj8MT1KTgJz/QBJW\nzSQ6f/u+x2UpzSnuekbQysNTRvSejcRv/xUpzqFZ8+bcdOONEVPlNcZf69ev5+5RI8nek8V1XfM5\nqpkzqOU9srgBq3L+1821a0MX9/TJC1p52wodPLu8IVkl0dx+xx2ceeaZAT2/iCxS1b5VHVdVn4BX\nAxTPQROR00RktYisE5FRoSr366+/5tLL/sGHH31EcaMOFBx2PiUdBgU1UYSFI5qyJp0o6H4exR1P\nYnuek7Fjx3LnnXeyc2eELRFrTAXy8vIYcdeduPKzuK93TtATRTi0SPIwpk82XVNLePLJJ5k/f35Y\n4qgqWYTlJ6avu+4E4HSgG3CJiHQLdrnTpk3jgQceINcTS2G3syltewwa3yDYxYaXCGWN2lDQ7WxK\nWh/FL8uWM2zYzWzbti3ckRlTpRdeeIGcnBxuPzyHdg1q96yulUmKUW47PI9DkpQnHn+M/Pzgz2a9\nv6qSRZSInCAiJ1Z0C1Jc/YB1qrpBVZ3AO8A5QSoLgO3btzN+/HjKGraisMsZeBIbB7O4yCMOXM26\nUdD5NHbtyeH5558Pd0TGVGr37t188cUXnNYqMFOKR7rYKPhn1zyydu/hq6++Cnn5VbVoxgGvU3EN\nQ4H2AY3IqyXeXld7ZQJ/GdooItcD1wM1HsSybNkyVJXSFr1qV+N1gHmSmuBs2JolS0O0cJMx1VRY\nWAhA6+TI7hYbSHvfa1FR4KZR91dVyaJQVYORDKpyoOT0l5Z4VZ0ITARvA3dNCuzWrRsORxSx25dT\n0v64epswHMXZxOb+QY/etjaGiWx751LaUlh//q/ufa/hmEcqAga9H1Am0Krc4wz+PFdVwGVkZHD1\n1VcRk72RpNUzcRTtCWZxf+FJTKMspfm+W8hHgquHmJ2rSF75MSmJCQwdOjS05RtzkFq0aMGJJ57I\nzM2JIU0YrZPL6NrQte8WqppNmQcmr25A47RGnHbaaSEps7yqus7mq2rIx5uLSDSwBjgJ71rfC4BL\nVXVFRa8JVNfZ7777jkcfe4yiwkJcjdriPKRn3W6/8LiJ3rOBhG1LoSSPnr16Mfr++2ncuA6/Z1Nn\nZGdnc+UVlxPlKuDWw3JoX0cbuQtcwksrUli+J4aHHnqIgQMHBuzc/nadrTRZhJOInAE8B0QBk1X1\n4cqOD+Q4i7y8PN5//33e/+ADSoqL8aQ0o7RJZ8rS2nkXG6oDpCSPmF2rid+zDnUW06FjR665+mqO\nPvpoG2dhapX169dzz92j2JO1i2u65HN0c2etGrVdlcyCKJ5fnsru0mjuuPNOzjgjsOOvan2yOFiB\nTBZ75eXl8dlnnzF9xgy2btmCRMdR2rANZY074E5pTq37iywrJSZ7EzG71xOVvx2Hw8GAAUdz9tln\n0a9fP0sSptbKyclhzOjRLF22jJ6NXfyjUwHNEiNv0sCDUeqGGZsSmLk5kZSUBjw07mEOP/zwgJdj\nySKAVJUlS5Ywc+ZM5n73HaUlJRCXjLNRO1xp7byXqSL1i9ZdRnTuH0Tv3kBM3hbwuMlo1YpTTzmF\n008/nSZN6thgQ1NvlZWVMW3aNN6Y/Dqu0hLObF3EmW2Kiatl7d+qsHBXLP+3PoXdxXDKKadw4403\nkpYWnHZMSxZBUlJSwg8//MDsL75gwYIFeNxuSEiltFE7XGkd0IQgr7ntD4+HqLxMYnZvIDZ3M+p2\n0bBRGoNPPonBgwfTqVMnq0WYOisrK4uXX36ZOXPmkBoHZ7cpZNAhkbGGRVVW7Inm/Q3JbMiLon27\nttx2+x1Bnw/KkkUI5Obm8t133/Hll1+ydOlSVBVPcjrORu0pa9w+tAsgqeIo3EXM7nXEZf+OuopJ\nSk7hxBMGceKJJ9KjRw+iomrZTyxjamD58uVMmjiRpcuW0ThBOa9NIcc0LyUqApPG2txoPtiQxMrs\naNKbNOaqq6/h1FNPDcnknpYsQiwrK4uvvvqKzz+fzfr167wjolNb4UrvjDu1JUhw/kLFVUx01jri\nd6+F4hxiYmI49thjGTx4MEceeWTEr+trTDCpKosWLWLSxFdZvWYtzRKVc9sWMKCZE0cEVK435EXx\n0cYklu2OoWFqAy6/4kqGDBlCXFxcyGKwZBFGmzZtYtasWcyc+Rl5ebkQl0xpelec6V0gOjB/BI7C\nLGK3LycmexOoh27dujNkyJkMGjSIxMQIXmPDmDBQVb7//nvemPw6GzZu4pAkb9Lo1zQ8SeP3/Cg+\n2pjIL1mxpCQnccmll3HuueeG5f+uJYsI4HK5+PHHH/nwo49YumQJEhVDaZMuOFv0QGOqt7xpVP52\n4rb8QlT+NuLi4xly5pmcddZZtG3bNrDBG1MHeTwevvvuO96Y/Dqbfv+DNikeLmpfwGFprpD0UdlZ\n7OCDDYn8tCOO5KRE/n7xJfztb38jKSkp+IVXwJJFhFmzZg3vvfcec+bMgagYSpodjrP5YX5PKyLF\nucRnzic6ZzON0tL4+0UXMWTIEJKTk4McuTF1j8fjYc6cObz+2iS279hJt7QyLu5QELQJCfOdwn83\nJfDV1gSio2O48KK/8/e//z0i1ti2ZBGhNm7cyKRJk5g3bx6amEZx66PwVLE0a3TOHyRsWUx8fCz/\nuOwyzj//fOLjq1czMcb8j9PpZMaMGbz91pvk5xdwUssSzm9fRFJMYL4XPQpzt8Xx3oZkisocnHHG\nGVx11VUR1WXdkkWEmzdvHk888SQ5Odl+HT9gwADuuusum4bDmCAoKChg8uTJ/HfaNFJilUs7eFfc\nq8mlqcyCKCavTmFdbhQ9ehzO7bffQbt27QIXdIBYsqgFcnNzmT9/Ph5P5SNN09LS6Nu3r42NMCbI\nVq9ezTPPPM3q1Ws4pnkpV3YuIP4ge6+qwtdb45i6NpnE5BRu+tdQTj311Ij9/2vJwhhjqsHtdjNl\nyhTefPMNWiQqw7rnkpHsX1tGSRlMXpXMTzvj6Hfkkdxz7700bNgwyBHXTKDW4DbGmHolKiqKK6+8\nkqeffoaS2EaM+6Uh6/Oqrl4UuoQnljZk/q54rrvuOh57/PGITxQHw2oWxhhTge3bt3P7bbeSk7WD\nSzoWkBh94O9LVZi5OZHNhbGMGTs2oFOIB5u/NYu6Md+2McYEQfPmzRn/wovccfttTF61pdJjY2Ki\neWjcQwwYMCBE0YWWJQtjjKlEeno6r70+ma1bK1+ss1GjRnXqstP+LFkYY0wV4uLiIrLbayhZA7cx\nxpgqWbIwxhhTJUsWxhhjqmTJwhhjTJUsWRhjjKmSJQtjjDFVsmRhjDGmSnVmug8R2QX8Hu446pAm\nQFa4gzDmAOxvM7DaqGp6VQfVmWRhAktEFvozX4wxoWZ/m+Fhl6GMMcZUyZKFMcaYKlmyMBWZGO4A\njKmA/W2GgbVZGGOMqZLVLIwxxlTJkoX5ExE5TURWi8g6ERkV7niM2UtEJovIThFZHu5Y6iNLFmYf\nEYkCJgCnA92AS0SkW3ijMmafN4HTwh1EfWXJwpTXD1inqhtU1Qm8A5wT5piMAUBV5wJ7wh1HfWXJ\nwpTXEthc7nGmb58xpp6zZGHKkwPss+5yxhhLFuZPMoFW5R5nAJWvUm+MqRcsWZjyFgCdRKSdiMQC\nFwMzwhyTMSYCWLIw+6hqGTAM+BxYCbynqivCG5UxXiLyH+BHoIuIZIrIP8MdU31iI7iNMcZUyWoW\nxhhjqmTJwhhjTJUsWRhjjKmSJQtjjDFVsmRhjDGmSpYsjKmEiLwiIvf7eew3InJtsGMyJhwsWZh6\nTUQ2iUixiOSLSI6IzBORG0XEAaCqN6rqQyGIwxKNiWiWLIyBs1Q1BWgDPAaMBF4Pb0jGRBZLFsb4\nqGquqs4A/g5cKSKHicibIjIOQEQaicgnIrJLRLJ92xn7naaDiMwXkVwRmS4iaXufEJGjfDWXHBFZ\nKiKDfPsfBgYCL4pIgYi86NvfVUS+EJE9vgWpLip3rjNE5DdfjWiLiNwV3E/H1HeWLIzZj6rOxzup\n4sD9nnIAb+CtgbQGioEX9zvmCuAa4BCgDBgPICItgU+BcUAacBfwoYikq+q9wHfAMFVNVtVhIpIE\nfAH8H9AUuAR4SUS6+8p5HbjBVyM6DPgqQG/fmAOyZGHMgW3F+6W+j6ruVtUPVbVIVfOBh4Hj93vd\nv1V1uaoWAvcDF/lWIPwHMFNVZ6qqR1W/ABYCZ1RQ/hBgk6q+oaplqroY+BC4wPe8C+gmIg1UNdv3\nvDFBY8nCmANryX6rsolIooi8KiK/i0geMBdo6EsGe5VfPOp3IAZogrc2cqHvElSOiOQAxwItKii/\nDdB/v+MvA5r7nj8fb6L5XUS+FZEBNXu7xlQuOtwBGBNpRORIvMnie6B/uafuBLoA/VV1u4j0An7h\nz4tGlV8PpDXeGkAW3iTyb1W9roJi95/RczPwraoOPuDBqguAc0QkBu9Mwe/tV7YxAWU1C2N8RKSB\niAzBu/b4FFX9db9DUvC2U+T4Gq7HHOA0/xCRbiKSCDwIfKCqbmAKcJaInCoiUSISLyKDyjWQ7wDa\nlzvPJ0BnEblcRGJ8tyNF5FARiRWRy0QkVVVdQB7gDtgHYcwBWLIwBj4WkXy8v+bvBZ4Brj7Acc8B\nCXhrCj8Bsw5wzL+BN4HtQDxwC4CqbgbOAe4BdvnKGs7//g8+D1zg62U13tcmcgreBai2+s73OBDn\nO/5yYJPvctiNeNtEjAkaW8/CGGNMlaxmYYwxpkqWLIwxxlTJkoUxxpgqWbIwxhhTJUsWxhhjqmTJ\nwhhjTJUsWRhjjKmSJQtjjDFVsmRhjDGmSv8P1bm8ob97zCUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ca18390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='Triceps_skin_fold_thickness', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('Triceps_skin_fold_thickness', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### serum_insulin\n",
    "餐后血清胰岛素（单位:mm）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'frequency')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cb19750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.serum_insulin, kde = False)\n",
    "plt.xlabel('serum_insulin')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "serum_insulin与标签之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VjBkzpk1xJ0vC+izUtxYuXIjf5yPQNUITVAO7A19BXz788CMCgegXNlNKfdfy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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cd20210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='serum_insulin', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('serum_insulin', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BMI\n",
    "体重指数（体重（公斤）/ 身高（米）^2）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'frequency')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cd42910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.BMI, kde = False)\n",
    "plt.xlabel('BMI')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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GDDmVMWOsHR/cnE/EGdRdgnobIMACYIwxJhW4GLizGe9RDZxhjDkRGAgMF5Eh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\nn4aCUkopPw0FpZRSfhoKSiml/P4/4BmKMykF6mwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1cb19fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target', y='BMI', data=train, hue=\"Target\")\n",
    "plt.xlabel('Diabetes', fontsize=12)\n",
    "plt.ylabel('BMI', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BMI=0？\n",
    "为缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d01f790>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Znr0jut85TLrmxbTciE8qpUp5WCzLB/FxwLmxLHfBy/2H+HzH6/Bh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\nFY1zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1ce2c990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "BMIDF = train.groupby(['BMI', 'Target'])['BMI'].count().unstack('Target').fillna(0)\n",
    "BMIDF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diabetes_pedigree_function，糖尿病家系作用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'frequency')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d15fd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###Diabetes_pedigree_function，糖尿病家系作用\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Diabetes_pedigree_function, kde = False)\n",
    "plt.xlabel('Diabetes_pedigree_function')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1d04a8d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xO36X7wV+iY/97I/P1I+kMSkxHpgdQni/me2Iz1AfEWUPwXv/Y/BXY1cBV+Hr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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d379690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Diabetes_pedigree_function', 'Target'])['Diabetes_pedigree_function'].count().unstack('Target').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'frequency')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hkiOBs4AP9H35OXDqCGvcGm8FbpqxPKn9AHhuVS2bcerhJH6+AP4RuKyqngY8k+73M1F9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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1d032850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(train.Age, kde = False)\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1db51410>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I5re7+29i8ttmtk9cvg+wPlted7/R3ZvdvbmhoaEcdRYRkSzynhQ1MwNuBla4+88Si2YD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\nEUSRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1e010910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DF = train.groupby(['Age', 'Target'])['Age'].count().unstack('Target').fillna(0)\n",
    "DF[[0,1]].plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b5c1410>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xzGX0U9dAclW2XCkuX77KVb/rREZGsnzpOho3sf61bdSkLgvmmSugq1ZspEbN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a19760e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b7b5e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bb1bc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bc2da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bb43e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bc1e790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b615190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bc4b510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1c0d3350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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e52qpBP2QZRW2AOvb9lnA1dXe5ViiZh1zm8b4MKOQX+rztjDLmKtqT1UdVVWrqmoVo/cl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\nS1Ln/g8cV0NBHrf59QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1b733110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in train.columns:\n",
    "    sns.distplot(train[feature],kde = False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:Anaconda3]",
   "language": "python",
   "name": "conda-env-Anaconda3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
